Investigating landfill contamination by visualizing geophysical data

Area of Science:

• Geophysics
• Environmental Science
• Data Science

Background:

• Chemical analysis of subsoil samples for landfill leakage contamination is time-consuming and expensive.
• Geophysical methods offer a potential alternative for detecting subsurface contamination.
• Sparse geophysical data, such as electrical resistivity, requires advanced processing for effective interpretation.

Purpose of the Study:

• To develop a method for identifying landfill leakage contamination without chemical subsoil analysis.
• To create a software package for visualizing and statistically analyzing geophysical data.
• To improve the understanding and detection of subsurface contamination phenomena.

Main Methods:

• Development of a software package for 3D visualization of geophysical data.
• Application of statistical analysis to detect anomalous values in acquired data.
• Utilizing kriging interpolation to create volumetric representations from sparse electrical resistivity data.
• Incorporation of uncertainty visualization to ensure reliable conclusions.

Main Results:

• The software facilitated the creation of 3D visualizations of subsoil electrical resistivity.
• Kriging interpolation provided volumetric representations of resistivity data.
• Uncertainty visualization was integrated to support robust analysis.
• The developed application enabled experts to better understand and validate their contamination detection method.

Conclusions:

• The developed software package effectively aids in identifying landfill leakage contamination.
• The application significantly eased the workload of geophysical experts in method development.
• The method provides a more efficient and less expensive alternative to traditional chemical analysis for subsoil contamination detection.